Basic characteristics Imports (targetcl. low risk rating)
71 
target objects 
The Automobile Database from UCI, containing several nominal values. The nominal values are replaced by natural numbers, artificially creating an ordering in the feature. The numbers are assigned according to the alphabetic order of the ordinal labels. The two classes are defined as those cars that have a assigned insurance risk rating (first feature) larger or smaller (and equal) than 0 respectively. The target class is low risk.Entries with missing values have been removed. Download matfile with Prtools dataset. 
88 
outlier objects 

25 
features 
Unsupervised PCA Imports (targetcl. low risk rating)
On the left, the PCA scatterplot is shown, on the right the retained variance for varying number of features.  
On the left, the PCA scatterplot is shown of data rescaled to unit variance, on the right the retained variance. 
Supervised Fisher Imports (targetcl. low risk rating)
On the left, the Fisher scatterplot is shown, on the right the ROC curve along this direction. 
Results Imports (targetcl. low risk rating)
The experiments are performed using dd_tools. A rudimentary explanation of the classifiers is given in the classifier section.
Classifiers  Preproc  

none  unit var  PCA 95\%  
Gauss  73.6 (0.7)  73.7 (0.8)  51.2 (1.6) 
Min.Cov.Determinant  NaN (0.0)  NaN (0.0)  48.5 (2.4) 
Mixture of Gaussians  77.2 (1.5)  79.2 (0.9)  64.8 (2.5) 
Naive Parzen  76.5 (1.5)  76.4 (1.4)  58.0 (2.5) 
Parzen  87.5 (1.0)  87.3 (0.9)  82.1 (1.3) 
kmeans  65.1 (2.4)  75.3 (1.4)  57.0 (2.4) 
1Nearest Neighbors  87.6 (1.0)  87.3 (1.0)  82.1 (1.4) 
kNearest Neighbors  87.6 (1.0)  87.3 (1.0)  82.1 (1.4) 
Nearestneighbor dist  84.5 (2.6)  85.0 (2.6)  72.0 (3.4) 
Principal comp.  73.1 (0.7)  64.6 (1.8)  51.0 (3.0) 
SelfOrgan. Map  78.8 (1.0)  81.6 (2.3)  72.3 (2.1) 
Autoenc network  68.7 (3.0)  76.2 (1.9)  53.2 (1.5) 
MST  87.1 (1.0)  87.5 (1.0)  82.7 (1.2) 
L_1ball  50.5 (31.5)  50.5 (31.5)  50.4 (2.4) 
kcenter  74.3 (2.6)  78.3 (3.1)  67.2 (3.9) 
Support vector DD  86.8 (1.4)  87.0 (1.0)  81.9 (1.4) 
Minimax Prob. DD  87.1 (0.9)  87.4 (1.1)  82.3 (1.3) 
LinProg DD  78.5 (0.9)  83.5 (1.0)  73.0 (1.4) 
Lof DD  81.0 (1.1)  81.2 (1.5)  78.8 (1.9) 
Lof range DD  77.5 (1.6)  82.6 (2.1)  76.2 (2.3) 
Loci DD  77.6 (1.0)  82.0 (0.5)  72.1 (1.4) 
Classifier projection spaces The first classifier projection spaces are obtained by computing the classifier label disagreements (setting the threshold on 10% target error) and applying an MDS on the resulting distance matrix between classifiers:




Classifier projection spaces The second versions of the classifier projection spaces are obtained by computing the classifier ranking disagreements and applying an MDS on the resulting distance matrix between classifiers:



